Self-hosted vs managed LLM platforms: what changes
There are two separate decisions buried inside "which LLM platform should we use," and conflating them leads to bad comparisons. The first is which model to run. The second is which layer manages the serving infrastructure underneath it, a fully managed platform like AWS Bedrock, Google Vertex AI, or Azure AI Foundry, or your own stack running vLLM, Ollama, or similar on hardware you rent or own. This post is about the second decision, the platform layer, not any specific vendor.
What a managed platform is actually buying you
Bedrock, Vertex, and Azure AI Foundry each bundle model access with the operational layer around it: autoscaling, load balancing, monitoring, a managed API surface, and usually a menu of models you can switch between without managing the serving stack yourself. You send requests, you get responses, and someone else is on call for the infrastructure underneath. That's a real and often underrated value, especially for a team without dedicated infrastructure staff, because a serving stack that stays up under production load and scales cleanly during a traffic spike is genuinely hard to build well.
What you give up is control over exactly where your data goes and exactly how the model is served, plus usage-based pricing that can be harder to predict at scale than a fixed hourly rate. Each of these platforms has its own data handling terms, region options, and pricing structure, and they differ enough between AWS, Google, and Microsoft that a comparison of any one against self-hosting has to be specific to that vendor. We've covered several of those individually: see our posts on on-premise vs cloud API cost and migrating off a public AI API for the vendor-specific mechanics.
What self-hosting actually changes
Running vLLM or Ollama on your own rented or owned hardware moves every one of those operational responsibilities onto your team: you choose the model, you tune the serving configuration, you monitor uptime, and you handle scaling decisions yourself rather than trusting a managed autoscaler. In exchange, you get a fixed, predictable hourly cost instead of usage-based billing, full control over which model runs and how it's configured, and, critically, no third party in the data path between your application and the model. For teams with EU data residency requirements or contracts that specify where processing happens, that last point is often the entire reason to self-host in the first place.
The honest cost of this trade is engineering time. A self-hosted stack needs someone who understands the serving layer well enough to keep it healthy, which is a real ongoing commitment, not a one-time setup task. It's worth being clear-eyed about that before committing, rather than discovering it after the fact when a model update or a traffic pattern change breaks something a managed platform would have absorbed silently.
Latency and network path matter more than they seem to at first
A managed platform's endpoint is usually close to the rest of that vendor's cloud, which is convenient if the rest of your stack already lives there and adds a network hop if it doesn't. A self-hosted deployment puts the model wherever you put the hardware, which means you can co-locate it with the rest of your application and cut that hop out entirely, or place it deliberately in a specific region for a data residency reason. Neither is automatically faster; it depends on where your other services already run and where your users are. It's worth measuring rather than assuming, especially for latency-sensitive interactive use rather than batch processing where the difference barely registers.
The framework for choosing
Three questions tend to settle this faster than a feature-by-feature comparison. First, does your data need to stay off a specific vendor's infrastructure, for regulatory, contractual, or competitive reasons? If yes, that alone often decides it toward self-hosting regardless of the other tradeoffs. Second, does your team have the operational capacity to run a serving stack, or would that capacity be better spent on the product itself? If your team is small and the workload isn't sensitive, a managed platform buys back real engineering time. Third, is your usage pattern steady enough that a fixed hourly cost beats usage-based pricing, or bursty enough that managed autoscaling is worth paying a premium for?
Most teams don't land on a pure answer. A common pattern is managed platforms for experimentation and lower-stakes internal tools, where the operational simplicity is worth the premium, and self-hosted infrastructure for the specific workloads where data sensitivity or cost at scale makes it worth the engineering investment. If you're weighing that split for your own workloads, our self-hosting cost guide and private LLM hosting pages go into the specifics of what running your own stack looks like day to day.